Open AccessAssessment Method for Residual Value of Lead-acid Batteries Based on PAM Clustering Algorithm
Author(s) -
Xiang Feng,
Xiaokun Zhang,
Yong Xiang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1549/3/032011
Subject(s) - lead–acid battery , residual , cluster analysis , algorithm , partition (number theory) , lead (geology) , medoid , computer science , service life , battery (electricity) , environmental science , materials science , mathematics , engineering , reliability engineering , artificial intelligence , physics , thermodynamics , geology , power (physics) , combinatorics , geomorphology
As the residual value of the lead-acid batteries is not effectively evaluated in the current scraping and recycling processes of the lead-acid batteries, the partition around medoids (PAM) clustering algorithm is adopted, and the scraped lead-acid batteries are classified with the changes in the temperature and charge-discharge occurring when the lead-acid batteries are in service as the characteristic parameters. Besides, the validity of the algorithm is validated using 300 lead-acid battery packs to be scrapped at the communication base station, from which the results showed that the residual value of the lead-acid batteries can be effectively distinguished by the partition around medoids algorithm according to the temperature and charge-discharge characteristic parameters.